Anomaly detection or one-class classification detects outliers or deviations in the input data stream to indicate malfunctions [1]} in an unsupervised fashion. Included in MLPerf Tiny v0.5 benchmark, applications of anomaly detection include diagnosis of industrial machinery [2]}[1]}[4]}, physiological disorders (e.g., heart attacks, seizures, etc.) [5]}, and climate conditions [6]}. The two most common network architectures for microcontroller-based anomaly detection are fully-connected autoencoders (FC-AE) and depthwise CNN. Table REF  lists some of the anomaly detectors developed for microcontrollers. Among the different techniques, DROCC can operate directly on raw audio, sensor data, and images [5]} without feature extraction. DROOC assumes that normal points lie on a low-dimensional linear manifold while points surrounding the normal points outside a threshold radius are outliers, which can be augmented in a generative adversarial manner into the training set. Other audio-based anomaly detectors generally operate on mel-spectrograms [2]}[1]}[4]}.
